pyspark 报错 Can not infer schema for type 需要自己传入schema from pyspark.sql.types import MapType, StringType, IntegerType, DoubleType, StructField, FloatType, StructType schema = StructType([ StructField("col1", IntegerType(), True), StructField("col2", IntegerType(), True), StructField("col3", IntegerType(), True)...
Initializing a single-column in-memory DataFrame in#PySparkcan be problematic compared to the Scala API. In the new blog post you can discover how to handle the "Can not infer schema for type..." error ?https://t.co/ctBQqbSsUk
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Caching is a key tool for iterative algorithms and fast interactive use.You can mark an RDD to be persisted using the persist() or cache() methods on it. The first time it is computed in an action, it will be kept in memory on the nodes. Spark’s cache is fault-tolerant – if ...
When schema is None, it will try to infer the schema (column names and types) from data, which should be an RDD of Row, or namedtuple, or dict. When schema is pyspark.sql.types.DataType or a datatype string, it must match the real data, or an exception will be thrown at runtime...